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README.md

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----
-license: mit
----
+---
+license: mit
+---
+---
+pretty_name: "Live or Lie — Live Streaming Room Risk Assessment (May/June 2025)"
+language:
+  - zh
+tags:
+  - live streaming risk assessment
+  - fraud detection
+  - weak supervision
+  - multiple-instance-learning
+  - behavior sequence
+license: other
+---
+
+# Dataset Card: Live Streaming Room Risk Assessment (May/June 2025)
+
+## Dataset Summary
+This dataset contains **live-streaming room interaction logs** for **room-level risk assessment** under **weak supervision**. Each example corresponds to a single live-streaming room and is labeled as **risky (> 0)** or **normal (= 0)**.
+
+The task is designed for early detection: each room’s action sequence is **truncated to the first 30 minutes**, and can be structured into **user–timeslot capsules** for models such as AC-MIL.
+
+
+## Languages
+  - Predominantly **Chinese (zh)**: user behaviors are presented in Chinese, e.g., "主播口播：...", these action descriptions are then encoded as action vectors via a **Chinese-bert**.
+
+
+## Data Structure
+Each room has a label and a sequence of **actions**:
+
+- `room_id` (`string`)
+- `label` (`int32`, {0,1,2,3}))
+- `patch_list` (`list` of tuples):
+  - `u_idx` (`string`): user identifier within a room
+  - `t` (`int32`): time index along the room timeline
+  - `l` (`int32`): capsule index
+  - `action_id` (`int32`): action type ID 
+  - `action_vec` (`list<float16>` or `null`): action features encoded from masked action descriptions
+  - `timestamp` (`string`): action timestamp
+  - `action_desc` (`string`): textual action descriptions
+  - `user_id` (`string`): user indentifier across rooms
+
+## Action Space
+The paper’s setup includes both viewer interactions (e.g., room entry, comments, likes, gifts, shares, etc.) and streamer activities (e.g., start stream, speech transcripts via voice-to-text, OCR-based visual content monitoring). Text-like fields are discretized as part of platform inspection/sampling.
+
+## Data Splits
+The paper uses two datasets (“May” and “June”), each with train/val/test splits.
+| Split | #Rooms | Avg. actions | Avg. users | Avg. time (min) |
+|------:|------:|-------------:|-----------:|----------------:|
+| May train | 176,354 | 709 | 35 | 30.0 |
+| May val   | 23,859  | 704 | 36 | 29.6 |
+| May test  | 22,804  | 740 | 37 | 29.7 |
+| June train| 80,472  | 700 | 36 | 30.0 |
+| June val  | 10,934  | 767 | 40 | 29.1 |
+| June test | 11,116  | 725 | 37 | 29.1 |
+
+## Quickstart
+Below we provide a simple example showing how to load the dataset.
+
+We use LMDB to store and organize the data. Please install the Python package first:
+```
+pip3 install lmdb
+```
+
+Here is a minimal demo for reading an LMDB record:
+```python
+import lmdb
+import pickle
+
+room_id = 0  # the room you want to read
+
+env = lmdb.open(
+    lmdb_path,
+    readonly=True,
+    lock=False,
+    map_size=240 * 1024 * 1024 * 1024,
+    readahead=False,
+)
+
+with env.begin() as txn:
+    value = txn.get(str(room_id).encode())
+    if value is not None:
+        data = pickle.loads(value)
+        patch_list = data["patch_list"]  # list of tuples: (u_idx, t, l, action_id, action_vec, timestamp, action_desc, global_user_idx)
+        room_label = data["label"]
+
+# close lmdb after reading
+env.close()
+```
+
+
+## Claim
+To ensure the security and privacy of TikTok users, all data collected from live rooms has been anonymized and masked, preventing any content from being linked to a specific individual. In addition, action vectors are re-encoded from the masked action descriptions. As a result, some fine-grained behavioral signals are inevitably lost, which leads to a performance drop for AC-MIL. The corresponding results are shown below.
+
+| Split | PR_AUC | ROC_AUC | R@P=0.9 | P@R=0.9 | R@FPR=0.1 | FPR@R=0.9 |
+|------:|------:|-------------:|-----------:|----------------:|----------------:|----------------:|
+| May   | 0.6518  | 0.9034 | 0.2281 | 0.2189 |   0.7527  | 0.3215 |
+| June   | 0.6120  | 0.8856 | 0.1685 | 0.1863 |   0.7111  | 0.3935 |
+
+---
+
+## Considerations for Using the Data
+
+Intended Use \
+•	Research on weakly-supervised risk detection / MIL in live streaming \
+•	Early-warning room-level moderation signals \
+•	Interpretability over localized behavior segments (capsule-level evidence) 
+
+Out-of-scope / Misuse \ 
+•	Do not use this dataset to identify, profile, or target individuals. \
+•	Do not treat predictions as definitive enforcement decisions without human review. 
+
+Bias, Limitations, and Recommendations \
+•	Sampling bias: negatives are downsampled (1:10); reported metrics and thresholds should account for this. \
+•	Domain specificity: behavior patterns are platform- and policy-specific; transfer to other platforms may be limited. \
+•	Weak supervision: only room-level labels are provided; interpretability at capsule level is model-derived.
+
+
+## License
+This dataset is licensed under CC BY 4.0:
+https://creativecommons.org/licenses/by/4.0/